We shall explain how to find compressed indexes to represent collections of very repetitive data in genomics (genome populations). We will present a number of results on compression but also on computation over compressed data.  We shall introduce the main conceptual principles that are followed to resolve such questions, becoming of major importance when we deal with more and more genomic data. The seminar will be accessible to a large public comprising experimentalists and theoreticians. 

Dipterans (flies, midges, and mosquitoes) determine segmentation along their main body axis during the blastoderm stage of development. In the initial phase of segmentation, maternal gradients are interpreted by the gap genes, the first set of zygotic genes that divide the embryo in antero-posterior regions (a pattern of stripes). Analysis of spatio-temporal expression and systematic knock-down of the gap genes in the scuttle fly Megaselia abdita reveals quantitative differences in gap gene cross-regulation between this species and Drosophila melanogaster. We have used mathematical modelling and reverse engineering of this gene network to gain insight into the regulatory mechanisms that underlie the observed differences in expression dynamics between the two species. Our models confirm that the qualitative structure of the gap gene network is conserved.  Differences in expression dynamics are fully explained by distinct initial placement of the gap gene expression domains, and quantitative modifications of specific interactions. Our models agree with the…

The development of DNA sequencing technologies allows us to collect large amounts of molecular data about the genome of each individual, and opens the possibility to predict drug response or evaluate the risk of various diseases from one's molecular identity. It also raises statistical and computational challenges, as the quantity of data collected per sample is usually far larger than the number of samples available to estimate predictive models. In this talk I will discuss some of these challenges and describe a few methods to attack them through regularization or change of representation, illustrated on applications in cancer prognosis and drug response prediction from gene expression and somatic mutations

Sexual reproduction is an extraordinarily widespread phenomenon that assures the production of new genetic combinations in nearly all eukaryotic lineages. Although the core features of sexual reproduction (meiosis and syngamy) are highly conserved, the control mechanisms that determine whether an individual is male or female are remarkably labile across eukaryotes. In genetically controlled sexual systems, gender is determined by sex chromosomes, which have emerged independently and repeatedly during evolution. Sex chromosomes have been studied in only a handful of classical model organism, and empirical knowledge on the origin and evolution of the sexes is still surprisingly incomplete. I will present how our group is exploiting the richness of sexual characteristics of the brown algae to gain novel insights into the functional and evolutionary interactions between the sex chromosomes and reproductive/life cycle features.

RNA viruses rapidly evade the host immune responses by changing targeted proteins. I will discuss how deep sequencing of HIV populations from many consecutive samples can be used to track virus evolution and quantify competing tendencies of immune escape and rapid replication. In a similar manner, the rapid evolution of influenza viruses necessitates the frequent updates of the seasonal influenza vaccines. We have used models of rapidly evolving populations to predict which strains of influenza virus are most likely to dominate future influenza seasons. Such models can inform the selection of virus strains used to produce influenza vaccines.

Numerous studies have documented the rapid evolution of mammalian transcriptional regulation, especially enhancers, and yet gene expression patterns are often conserved between species. How stable gene expression is maintained by rapidly evolving regulatory landscapes is a fundamental question in evolutionary genetics.We tested the contributions of both complexity and conservation of regulatory elements on gene expression evolution, using an integrated dataset of active promoters, enhancers and gene expression output from the same liver samples across twenty mammalian species. Our methodology captures regulatory activities that range from essential to dispensable, and from highly-conserved across mammals to present in only one species. This analysis revealed that gene expression levels and stability are reflected by the complexity of their regulatory landscape, both within a single species and across mammals. Regulatory activities conserved across placental mammals exert a powerful stabilizing effect, associating with gene expression levels that are simultaneously high and evolutionarily stable. In contrast, recently-evolved enhancers…

We introduce an accurate and parameter free approach to estimate the probability density for non-uniform samples embedded in high dimensional and curved manifolds.The estimator uses only distances between the points and not their coordinates and does not require specifying the functional form of the probability. Moreover, it provides an approximately unbiased measure of the uncertainty of the estimate.The availability of an error estimate allows distinguishing genuine probability peaks from statistical fluctuations due to finite sampling. This is an essential ingredient for  finding the meaningful  peaks and the saddles between them.  Based on this information, we then reconstruct a hierarchical topography of the probability density, which provides a compact representation of the data set.We demonstrate the usefulness of the approach in classification problems in functional magnetic resonance imaging, proteomics and metagenomics.